Visual Analytics Dashboard for Real-Time Monitoring of Industrial Equipment

Mahmood, Ahmed; Mwafaq, Lara; Al-Shammari, Ban; Nema, Abbas; Hussein, Adil

doi:10.25673/123739

Proceedings of International Conference on Applied Innovation in IT · 2026/03/31 · Vol. 14 · Issue 1 · pp. 1351–1357

Visual Analytics Dashboard for Real-Time Monitoring of Industrial Equipment

Ahmed Hadi Mahmood, Lara Mwafaq, Ban Safir Khalaf Al-Shammari, Abbas Nuaithal Nema, Adil Abbas Majeed and Abdullah Abed Hussein

📄 Download PDF DOI: 10.25673/123739

Abstract

The current paper is the design and implementation of a Visual Analytics Dashboard to monitor the industrial equipment in real-time, combining the data streams of the IoT-based sensor with the edge analytics and machine learning engine. The suggested system will provide the maintenance teams with a high-resolution dashboard and enables visualization of the live data, anomaly detection, and human-in-the-loop interaction. A sensor emulator, edge gateway and Python-based ML engine were created to form an experimental testbed, which recorded vibration, voltage, and temperature measurements in real-time. Statistical thresholding and pattern learning are used to detect anomalies, and the dashboard includes interactive zooming, annotation, and alert recognition features.The proposed dashboard proves to be much more responsive, more usable and more successful in detecting anomalies compared to traditional SCADA-based systems. Compared performance analysis indicates that there is a 47% decrease in latency, 16% improvement in the accuracy of anomaly detection, and 38% improvement in the score of usability. The architecture encourages scalability, edge processing, and operator-oriented feedback loops, which is why it is feasible in predictive maintenance in the Industry 4.0 environment. The study makes available a repeatable framework and visualization modal in real-time monitoring of conditions in smart manufacturing. Additional enhancements can include adaptive model of learning, cross-platform implementation and NLP-based operator suggestions in the future.

Keywords

Visual Analytics Anomaly Detection Industrial IoT Edge Gateway Machine Learning Real-time Dashboard Predictive Maintenance SCADA.

References

P. Mallioris, E. Aivazidou, and D. Bechtsis, “Predictive maintenance in Industry 4.0: A systematic multi-sector mapping,” CIRP Journal of Manufacturing Science and Technology, vol. 50, pp. 80–103, 2024.
K. Varalakshmi and J. Kumar, “Optimized predictive maintenance for streaming data in industrial IoT networks using deep reinforcement learning and ensemble techniques,” Scientific Reports, vol. 15, no. 1, p. 27201, 2025.
S. K. Bhoi et al., “Intelligent data-driven condition monitoring of power electronics systems using smart edge–cloud framework,” Internet of Things, vol. 26, p. 101158, 2024.
O. Khattach, O. Moussaoui, and M. Hassine, “End-to-end architecture for real-time IoT analytics and predictive maintenance using stream processing and ML pipelines,” Sensors, vol. 25, no. 9, p. 2945, 2025.
M. R. Islam et al., “Strategies for evaluating visual analytics systems: A systematic review and new perspectives,” Information Visualization, vol. 23, no. 1, pp. 84–101, 2024.
L. T. Nguyen and M. Wiese, “TAM and IS success model on digital library use,” Library Management, vol. 24, no. 1/2, pp. 173–185, 2003, doi: 10.1108/01435120310454592.
Y. Zhang, H. Li, and X. Chen, “Artificial intelligence–enabled cloud security: Opportunities and challenges,” Digital Communications and Networks, vol. 11, no. 2, pp. 55–66, 2025, doi: 10.1016/j.dcan.2025.01.005.
J. Suschnigg et al., “MANDALA—Visual exploration of anomalies in industrial multivariate time series data,” in Computer Graphics Forum, vol. 44, no. 1, p. e70000, Feb. 2025.
T. Khan et al., “Data-driven digital twin framework for predictive maintenance of smart manufacturing systems,” Machines, vol. 13, no. 6, p. 481, 2025.
O. A. Ekle and W. Eberle, “Anomaly detection in dynamic graphs: A comprehensive survey,” ACM Transactions on Knowledge Discovery from Data, vol. 18, no. 8, pp. 1–44, 2024.
A. E. Bondoc, M. Tayefeh, and A. Barari, “Learning phase in a LIVE digital twin for predictive maintenance,” Autonomous Intelligent Systems, vol. 2, no. 1, p. 13, 2022.
W. Fernandes Jr, K. S. Komati, and K. Assis de Souza Gazolli, “Anomaly detection in oil-producing wells: A comparative study of one-class classifiers in a multivariate time series dataset,” Journal of Petroleum Exploration and Production Technology, vol. 14, no. 1, pp. 343–363, 2024.
P. Trirat, Y. Nam, T. Kim, and J. G. Lee, “ANOVIZ: A visual inspection tool of anomalies in multivariate time series,” in Proc. AAAI Conf. Artificial Intelligence, vol. 37, no. 13, pp. 16488–16490, 2023.
N. H. Abd Wahab et al., “Systematic review of predictive maintenance and digital twin technologies: Challenges, opportunities, and best practices,” PeerJ Computer Science, vol. 10, p. e1943, 2024.
R. Sharma, P. Gupta, and A. Singh, “Human–computer interaction frameworks for secure digital adoption,” International Journal of Human–Computer Interaction, vol. 41, no. 7, pp. 845–862, 2025, doi: 10.1080/10447318.2025.2495843.
M. T. Sadeghi and H. Alzubaidi, “Fortifying wireless sensor networks using SVM for advanced intrusion detection and attack prevention,” InfoTech Spectrum: Iraqi Journal of Data Science, vol. 2, no. 2, pp. 1–13, 2025, doi: 10.51173/ijds.v2i2.24.
A. Bida and H. A. Naser, “Diagnostic of osteoporosis using backpropagation neural networks,” Journal of Techniques, vol. 7, no. 2, pp. 10–20, 2025, doi: 10.51173/jt.v7i2.2597.
F. Tahseen, A. Talib, and D. A. Al-Ubaidi, “An improved method for retrieving fashion images based on parsing the image contents,” Communications in Computer and Information Science, vol. 2096, pp. 288–301, 2024, doi: 10.1007/978-3-031-62814-6_21.